def start_program():
input_filename = "C:/Users/aNsHuL's pC/CSIRE Stony Brook 2019 Summer/oasis_longitudinal.csv"
df = pd.read_csv(input_filename)
predictor_object = Predictor(df)
predictor_object.prepare_dataframe()
predictor_object.fit_models()
print("Please enter your information to start the program")
gender = input("What is your gender(M or F): ")
if gender == "M":
gender_num = 1
gender = gender_num
elif gender == "F":
gender_num = 0
gender = gender_num
while True:
try:
age = int(input("Please enter age (whole numbers until 126): "))
except ValueError:
print('Please enter only whole numbers for age')
else:
if 0 < age < 126:
break
while True:
try:
education = int(input("Enter number of years of education (whole numbers until 24): "))
except ValueError:
print('Please enter only whole numbers for education')
else:
if 0 < education < 25:
break
while True:
try:
ses = int(input("Enter socio economic status (1 - 5): "))
except ValueError:
print('Please enter only whole numbers between 1 and 5')
else:
if 0 < ses < 6:
break
while True:
try:
mmse = int(input("Enter mini mental status examination score(0 - 30): "))
except ValueError:
print('Please enter only whole numbers between 1 and 30')
else:
if 0 <= mmse <= 30:
break
while True:
try:
cdr = int(input("Enter clinical dementia rating(0 - 3): "))
except ValueError:
print('Please enter only whole numbers between 0 and 3')
else:
if 0 <= cdr <= 3:
break
while True:
try:
etiv = int(input("Enter total intracranial volume(1100 - 2010): "))
except ValueError:
print('Please enter only whole numbers between 1100 and 2010')
else:
if 1100 <= etiv <= 2010:
break
while True:
try:
wbv = float(input("Enter normalized whole brain volume(0.6 - 0.9): "))
except ValueError:
print('Please enter values between 0.6 and 0.9')
else:
if 0.6 <= wbv <= 0.9:
break
while True:
try:
asf = float(input("Enter normalized atlas scaling factor: (0.8 - 1.7): "))
except ValueError:
print('Please enter values between 0.8 and 1.6')
else:
if 0.8 <= asf <= 1.7:
break
y_predicted_logistic, y_predicted_dt, y_predicted_rf, y_predicted_svm, y_predicted_ada = predictor_object.predict(gender, age, education, ses, mmse, cdr, etiv, wbv, asf)
if y_predicted_logistic[0] == 0:
print("Based on the LOGISTIC REGRESSION, it is UNLIKELY that you will be diagnosed with Alzheimer's")
elif y_predicted_logistic[0] == 1:
print("Based on the LOGISTIC REGRESSION, it is LIKELY that you will be diagnosed with Alzheimer's")
if y_predicted_svm[0] == 0:
print("Based on the SUPPORT VECTOR MACHINE, it is UNLIKELY that you will be diagnosed with Alzheimer's")
elif y_predicted_svm[0] == 1:
print("Based on the SUPPORT VECTOR MACHINE, it is LIKELY that you will be diagnosed with Alzheimer's")
if y_predicted_dt[0] == 0:
print("Based on the DECISION TREE CLASSIFIER, it is UNLIKELY that you will be diagnosed with Alzheimer's")
elif y_predicted_dt[0] == 1:
print("Based on the DECISION TREE CLASSIFIER, it is LIKELY that you will be diagnosed with Alzheimer's")
if y_predicted_rf[0] == 0:
print("Based on the RANDOM FOREST CLASSIFIER, it is UNLIKELY that you will be diagnosed with Alzheimer's")
elif y_predicted_rf[0] == 1:
print("Based on the RANDOM FOREST CLASSIFIER, it is LIKELY that you will be diagnosed with Alzheimer's")
if y_predicted_ada[0] == 0:
print("Based on the ADAPTIVE BOOSTING, it is UNLIKELY that you will be diagnosed with Alzheimer's")
elif y_predicted_ada[0] == 1:
print("Based on the ADAPTIVE BOOSTING, it is LIKELY that you will be diagnosed with Alzheimer's")
